Quantifying mechanisms of rapid adaptation to predict responses to climate change

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Intraspecific variation can arise through multiple mechanisms, and such variation can be acted on by selection to promote adaptation to new environments. Climate change is a global issue that is already impacting species and ecosystems, highlighting an immediate need to understand and predict how species will adapt to future changes. With advances in genomic technology, landscape genomics is becoming commonplace for identifying genetic variants associated with climate adaptation across many taxa. While these data can be informative for predicting responses to future climate scenarios, these methods are often based on correlational information between genotype and environment. Many genomic studies do not directly examine phenotype and fail to account for mechanisms such as plasticity and mutation in climate adaptation. Therefore, a full picture of the evolutionary responses to climate change is currently lacking, and an eco-evolutionary model that incorporates knowledge of multiple mechanisms involved in adaptation is needed. To address these gaps in the field, this research program will combine field and laboratory research on Atlantic salmon (Salmo salar) which represent an ecologically, culturally, and economically significant species that continues to experience population declines across its range with climate being a potential factor. Through this research program, five graduate and four undergraduate students will be trained in field and laboratory techniques related to fish collection and physiology, molecular analyses, and bioinformatics and statistical analyses. In addition, students will also receive training in the preparation of manuscripts for publication and other avenues of science communication. Overall, the integration of multiple approaches and the development of models to robustly estimate responses to climate change will provide key tools for evolutionary biologists as well as conservation and management to help understand and predict future changes.